Isolating intrinsic noise sources in a stochastic genetic switch

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Gene regulatory networks (GRNs) are fundamental to cellular function.
• Stochasticity, or noise, plays a crucial role in biological processes.
• The stochastic mutual repressor model is a simplified GRN with bistable behavior.

Purpose of the Study:

• To analyze the impact of different intrinsic noise sources on a stochastic mutual repressor gene circuit model.
• To compare the system's behavior with and without protein noise.
• To understand how noise affects transitions between stable states in a bistable gene circuit.

Main Methods:

• Perturbation methods were used for mathematical analysis.
• Monte Carlo simulations were employed to model stochastic processes.
• The study systematically removed protein production and degradation noise.

Main Results:

• The stochastic mutual repressor model exhibits bistability with two stable fixed points and an unstable saddle.
• On long timescales, metastable transitions between stable states can occur.
• Removing protein noise sources led to significant differences in the system's random dynamics compared to models with protein noise.

Conclusions:

• Intrinsic noise, particularly fluctuations in protein levels, significantly influences the dynamics of gene regulatory networks.
• Understanding noise sources is critical for accurately modeling biological systems.
• The study underscores the importance of considering all relevant noise components in GRN analysis.